RPI-MDLStack: Predicting RNA–protein interactions through deep learning with stacking strategy and LASSO

Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy

(2022) Minghui Wang et al.   KNOWLEDGE-BASED SYSTEMS

Deciphering molecular interactions by proximity labeling

(2021) Wei Qin et al.   NATURE METHODS

Prediction of protein-protein interactions based on elastic net and deep forest

(2021) Bin Yu et al.   EXPERT SYSTEMS WITH APPLICATIONS

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction

(2021) Jingjing Wang et al.   BMC BIOINFORMATICS

Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis

(2021) Xue Wang et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

(2021) Yushuang Liu et al.   BIOINFORMATICS

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

(2020) Hai-Cheng Yi et al.   BMC BIOINFORMATICS

Development of a new oligonucleotide block location-based feature extraction (BLBFE) method for the classification of riboswitches

(2020) F. Golabi et al.   MOLECULAR GENETICS AND GENOMICS

LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick

(2020) Shao-Wu Zhang et al.   ANALYTICAL BIOCHEMISTRY

DNNAce: Prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion

(2020) Bin Yu et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

(2020) Cheng Chen et al.   COMPUTERS IN BIOLOGY AND MEDICINE

RNA-binding protein recognition based on multi-view deep feature and multi-label learning

(2020) Haitao Yang et al.   BRIEFINGS IN BIOINFORMATICS

Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction

(2019) Qiguo Dai et al.   Frontiers in Genetics

RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

(2019) Cheng Peng et al.   INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Recent methodology progress of deep learning for RNA–protein interaction prediction

(2019) Xiaoyong Pan et al.   Wiley Interdisciplinary Reviews-RNA

LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion

(2019) Cheng Chen et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

LPI-BLS: Predicting lncRNA–protein interactions with a broad learning system-based stacked ensemble classifier

(2019) Xiao-Nan Fan et al.   NEUROCOMPUTING

CRIP: predicting circRNA-RBP interaction sites using a codon-based encoding and hybrid deep neural networks

(2019) Kaiming Zhang et al.   RNA

A deep learning framework to predict binding preference of RNA constituents on protein surface

(2019) Jordy Homing Lam et al.   Nature Communications

Approximate Sparse Multinomial Logistic Regression for Classification

(2019) Koray Kayabol   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

(2018) Bin Yu et al.   BMC GENOMICS

Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

(2018) Wenying Qiu et al.   JOURNAL OF THEORETICAL BIOLOGY

Deep learning for sentiment analysis: A survey

(2018) Lei Zhang et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Fault Detection Based on Modified Kernel Semi-Supervised Locally Linear Embedding

(2018) Yingwei Zhang et al.   IEEE Access

Sparse view cone beam X-ray luminescence tomography based on truncated singular value decomposition

(2018) Peng Gao et al.   OPTICS EXPRESS

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning

(2018) Jinfang Zheng et al.   Scientific Reports

Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

(2018) Xiaoying Wang et al.   BIOINFORMATICS

UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components

(2018) Xiaowen Cui et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

(2018) Han Shi et al.   GENOMICS

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method

(2018) Lei Wang et al.   JOURNAL OF THEORETICAL BIOLOGY

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

(2018) Yi Xiong et al.   Frontiers in Microbiology

Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

(2018) Zhao-Hui Zhan et al.   Frontiers in Genetics

IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction

(2016) Xiaoyong Pan et al.   BMC GENOMICS

Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs

(2016) Marta Melé et al.   GENOME RESEARCH

Unique features of long non-coding RNA biogenesis and function

(2016) Jeffrey J. Quinn et al.   NATURE REVIEWS GENETICS

NPInter v3.0: an upgraded database of noncoding RNA-associated interactions

(2016) Yajing Hao et al.   Database-The Journal of Biological Databases and Curation

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

(2015) Bin Liu et al.   NUCLEIC ACIDS RESEARCH

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

(2015) V. Suresh et al.   NUCLEIC ACIDS RESEARCH

PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition

(2014) Wei Chen et al.   ANALYTICAL BIOCHEMISTRY

Mutual information and the fidelity of response of gene regulatory models

(2014) Omar P Tabbaa et al.   PHYSICAL BIOLOGY

catRAPID omics: a web server for large-scale prediction of protein-RNA interactions

(2013) F. Agostini et al.   BIOINFORMATICS

Computational prediction of associations between long non-coding RNAs and proteins

(2013) Qiongshi Lu et al.   BMC GENOMICS

Long Noncoding RNAs: Past, Present, and Future

(2013) J. T. Y. Kung et al.   GENETICS

Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction

(2012) Xiaotong Lin et al.   PROTEOMICS

The Non-Coding Oncogene: A Case of Missing DNA Evidence?

(2012) Puja Shahrouki et al.   Frontiers in Genetics

Predicting RNA-Protein Interactions Using Only Sequence Information

(2011) Usha K Muppirala et al.   BMC BIOINFORMATICS

Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise

(2011) T. Tony Cai et al.   IEEE TRANSACTIONS ON INFORMATION THEORY

RNA targets of wild-type and mutant FET family proteins

(2011) Jessica I Hoell et al.   NATURE STRUCTURAL & MOLECULAR BIOLOGY

A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response

(2010) Maite Huarte et al.   CELL

On Position-Specific Scoring Matrix for Protein Function Prediction

(2010) Jong Cheol Jeong et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

PRIDB: a protein-RNA interface database

(2010) B. A. Lewis et al.   NUCLEIC ACIDS RESEARCH

Drosophila dosage compensation: a complex voyage to the X chromosome

(2009) M. E. Gelbart et al.   DEVELOPMENT

Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals

(2009) Mitchell Guttman et al.   NATURE